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Agent observability and evaluation, explained

Everything you need to understand agent observability and evaluation.

Agent fundamentals

October 30, 2026

How to build an AI agent, with evals from day one

Build an AI agent by starting with the simplest design that works: a model, tools and a stop condition in a loop. Write 20 to 50 evals before you scale it.

October 30, 2026

What are AI agents?

An AI agent is a system where an LLM decides its own steps and calls tools in a loop until a task is done. How that differs from a workflow, and when to use one.

October 30, 2026

What is agentic AI?

Agentic AI is AI that pursues a goal with limited supervision, planning steps and calling tools. Sources disagree on how it differs from an AI agent.

October 30, 2026

What is an agentic workflow?

An agentic workflow is a multi-step process where an LLM plans or routes steps. Providers split it into fixed workflows and self-directed agents.

Agent observability

October 30, 2026

What is agent observability?

Agent observability records every step an AI agent takes on a task as one trace, so you can see loops, wrong tools, bad tool arguments and cost per task.

October 30, 2026

What is LLM observability?

LLM observability records each request to an LLM app as a trace and scores the output, so you can see why an answer was wrong even when the app was up.

Agent evaluation

October 30, 2026

What is LLM evaluation?

LLM evaluation tests whether a model or LLM app gives good outputs, by grading its answers on test cases with code checks, LLM judges or human review.

October 30, 2026

What is TEVV for AI?

TEVV is test, evaluation, verification and validation. NIST's AI RMF uses it for the checks run on an AI system at every stage of its lifecycle.

Prompt and context engineering

October 30, 2026

What is few-shot prompting?

Few-shot prompting puts a few input and output examples in the prompt so the model copies the pattern. Here is what providers advise and what research shows.

Agent security and governance

October 30, 2026

What are AI guardrails?

AI guardrails are runtime checks around a model that inspect inputs, outputs and agent actions, then block, rewrite or flag anything outside your policy.

Agent frameworks

October 30, 2026

What is LangGraph? How it works and how to evaluate it

LangGraph is LangChain's open-source framework for building long-running, stateful AI agents as graphs of nodes with shared state, persistence and human pauses.

October 30, 2026

What is OpenAI Agents SDK? How it works and how to evaluate it

The OpenAI Agents SDK is a Python framework for agents, handoffs and guardrails with built-in tracing. How the run loop works, its limits, and how to evaluate it.

October 30, 2026

What is Pydantic AI? How it works and how to evaluate it

Pydantic AI is an open-source Python agent framework from the Pydantic team. Agents return validated, typed output and emit OpenTelemetry traces.

Agent protocols

October 30, 2026

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard that lets AI apps connect to tools and data through MCP servers, using JSON-RPC messages.

Benchmarks

October 30, 2026

What is SWE-bench? Verified, Pro and how scores work

SWE-bench tests whether an AI agent can fix real GitHub issues. Learn how a task is scored, what Verified and Pro are, and why their scores differ.